This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction. In addition, we have designed practice exercises that will give you hands-on experience implementing these data science models on data sets. These practice exercises will teach you how to implement machine learning algorithms with PyTorch, open source libraries used by leading tech companies in the machine learning field (e.g., Google, NVIDIA, CocaCola, eBay, Snapchat, Uber and many more).
Offered By
Introduction to Machine Learning
Duke UniversityAbout this Course
Learner Career Outcomes
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Learner Career Outcomes
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Syllabus - What you will learn from this course
Simple Introduction to Machine Learning
Basics of Model Learning
Image Analysis with Convolutional Neural Networks
Recurrent Neural Networks for Natural Language Processing
Reviews
- 5 stars74.61%
- 4 stars20.72%
- 3 stars2.76%
- 2 stars0.65%
- 1 star1.24%
TOP REVIEWS FROM INTRODUCTION TO MACHINE LEARNING
Thanks Coursera and Duke University for this course. It is very insightful to get understood the basics of ML and applied ML in numerous fields. It really made me to move ahead with ML domain.
The exercises are too much toy for people who already have certain knowledge of machine learning and are not feed forward for students that has no experience solving machine learning problems.
The course covers all the topic's regarding the machine learning and has an excellent explanation of concepts and the slides are very easy to understand thank you for such a wonderful course !
I felt that I took the best descition in taking this course, because the professors took this course with atmost clarity and made even the difficult concepts understand easily.
Thank you Professors
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